Abstract
We present a line-scan stereo system and descriptor-based dense stereo matching for high-performance vision applications. Additionally we introduce a post-processing step based on total variation (TV) regularization for robust disparity estimation. Descriptor-based matching utilizes the Stochastic Binary Local Descriptor (STABLE). The performance of STABLE was shown to be superior to other binary descriptors, both w.r.t. stereo reconstruction quality as well as runtime performance. Regularized estimation of disparity maps is suggested as a hierarchical and iterative post-processing procedure where the Pseudo-Huber-TV norm was employed. We describe the hardware setup consisting of two line-scan cameras mounted in a car trailer and observing the road surface. Presented are results of 3D road surface reconstruction which are used in applications of road infrastructure maintenance.
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